Journal of Loss Prevention in The Process Industries最新文献

{"title":"Design and implementation of a VR-based evacuation simulation system: A case study with impaired people","authors":"Jiayue Wang ,&nbsp;Huanyu Wang ,&nbsp;Liangchang Shen ,&nbsp;Liping Kou ,&nbsp;Yunhe Tong","doi":"10.1016/j.jlp.2025.105888","DOIUrl":"10.1016/j.jlp.2025.105888","url":null,"abstract":"<div><div>In the process industries, incident investigations repeatedly show that deficiencies in evacuation guidance systems contribute to casualties during fires, explosions, and toxic releases. Effective guidance that performs reliably under such hazardous conditions is therefore a critical component of process safety and loss prevention. However, traditional evacuation drills often lack realism, repeatability, and inclusivity, limiting their value for hazard mitigation and safety system optimisation. This study develops a Virtual Reality (VR)-based evacuation simulation platform designed for both safety training and empirical evaluation of evacuation guidance strategies. The system models a five-story enclosed building with configurable layouts, emergency broadcasts, and multiple signage types (graphic-only, text-based, combined, and enhanced with directional or supplementary cues). It enables safe, repeatable testing of human response to process-related emergency scenarios, recording detailed behavioural metrics such as movement trajectories, decision points, and evacuation time. In the case study, 88 % of participants deviated from the designated route at least once, and several intersections showed 10–30 misjudgements. Misjudgement frequency strongly predicted evacuation time, and the optimal signage–broadcast configurations substantially reduced average evacuation times for both hearing-impaired and cognitively impaired participants. The results revealed frequent navigation errors and highlighted guidance combinations tailored to different user needs, such as prominent door and wall signage for hearing-impaired individuals and early verbal alerts aligned with visual cues for those with cognitive impairments. This work introduces a practical tool for loss prevention in the process industries, supporting the design and verification of evacuation systems, training programs, and architectural layouts in alignment with process safety objectives.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"100 ","pages":"Article 105888"},"PeriodicalIF":4.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145796783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inhibitory effects of varying kaolin concentrations on CH4/H2 explosion characteristics","authors":"Shanshan Liu,&nbsp;Dongxu Huang,&nbsp;Yong Pan,&nbsp;Zhenhua Wang,&nbsp;Juncheng Jiang","doi":"10.1016/j.jlp.2025.105889","DOIUrl":"10.1016/j.jlp.2025.105889","url":null,"abstract":"<div><div>This study employs laser schlieren imaging and pressure measurement techniques to investigate the impact of kaolin (Ko) on the explosion behavior of CH₄/H₂ mixtures at various equivalence ratios (0.8, 1.0 and 1.2) and H₂ contents (0, 0.3 and 0.9). Results indicate that the inhibitory effect of Ko first increases and then decreases with increasing concentration, achieving optimal suppression at 175 g/m³. Under the same equivalent ratios (<em>φ</em>), the reduction in maximum explosion pressure (<em>P</em>_max) becomes more pronounced with higher H₂ addition (<em>R</em>). At constant <em>R</em> but varying <em>φ</em>, the suppression effect exhibits a different trend: when <em>R</em> = 0 and 0.3, optimal suppression occurs at <em>φ</em> = 0.8. Whereas at <em>R</em> = 0.9, the optimal suppression effect is observed at <em>φ</em> = 1.0, corresponding to a 30.27 % reduction in <em>P</em>_max. As the Ko concentration increases, dust enhances flow field instability, thereby accelerating the transformation of the flame structure. Meanwhile, higher hydrogen addition (<em>R</em>) intensifies chemiluminescence, and heated Ko particles to emit strong intense thermal radiation. The combined effect of these two factors causes the flame to appear bright white-yellow. A coupled analysis of flame propagation and pressure evolution reveals that, despite differences in <em>φ</em>, the coupled evolution of flame and pressure remains highly similar under the same <em>R</em>. The main distinctions arise in the timing of critical flame development stage and flame brightness. Overall, Ko suppresses explosions primarily through physical mechanisms such as endothermic cooling, dilution and isolation effects, and thermal radiation shielding, and it exhibits particularly strong suppression at high H₂ additions.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"100 ","pages":"Article 105889"},"PeriodicalIF":4.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145836688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explosion parameters of aviation kerosene/nano aluminum mixture at initial high temperature and pressure","authors":"Yue Wang ,&nbsp;Zhiguo Chang ,&nbsp;Qi Zhang","doi":"10.1016/j.jlp.2026.105919","DOIUrl":"10.1016/j.jlp.2026.105919","url":null,"abstract":"<div><div>JP10 (95(w)%) and nm aluminum (5(w)%) in air mist, as a special fuel used in underground mining of coalbed methane, the explosion hazard is the basis of safety design. In this study, the explosion pressure, the maximum rate of explosion pressure rise and the lower limit of the explosion concentration of aviation kerosene JP10 (95(w)%) and nm-aluminum powder (5(w)%) mist under different initial pressures and initial temperatures were observed by using a 20 L mist explosion experimental device. Change laws of the experimental peak explosion pressures of the JP10 (95(w)%) and nm aluminum (5(w)%) in air mist with concentration, with initial pressure and initial temperature have been found respectively. The experimental peak explosion pressures of the JP10 (95(w)%) and nm aluminum (5(w)%) in air mist at the concentration 500 g/m³ increase with the initial pressure and decrease as the initial temperature increases. The experimental lower explosion concentration limits of the fuel (JP10, 95(w)% nm aluminum, 5(w)% in air) mist decrease as the initial temperature increases within the initial temperature range from 30 °C to 80 °C.The lower explosion limit of the fuel-air mixture JP10 (95w%) and nm AL powder (5w%) decreases as the initial pressure increases from 0.1 MPa to 0.3 MPa.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"100 ","pages":"Article 105919"},"PeriodicalIF":4.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multidimensional quantitative modeling fusion analysis of safety risks in hydrogen refueling stations: A case study of a station in Beijing","authors":"Zhen Liang ,&nbsp;Yunhao Yang ,&nbsp;Yingjian Wang ,&nbsp;Meng Zhang ,&nbsp;Yufeng Zhuang","doi":"10.1016/j.jlp.2025.105865","DOIUrl":"10.1016/j.jlp.2025.105865","url":null,"abstract":"<div><div>Hydrogen, as a zero-emission clean energy source with wide availability and pollution-free combustion characteristics, also exhibits high flammability and explosiveness, posing potential fire and explosion hazards. With the rapid global development of the hydrogen energy industry, Hydrogen Refueling Stations (HRSs), as critical infrastructure for fuel cell vehicles, face significant safety operation challenges. To address this, we develop an a multidimensional quantitative modeling and integrated analysis framework for safety risks in HRSs. First, Hazard and Operability Study (HAZOP) analysis is used to identify hazard sources and extract key deviations and key scenarios that may lead to safety risks. Next, a Bow-Tie model is employed to identify and model top events, intermediate events, and basic events, clearly outlining accident evolution pathways. To quantitatively evaluate event likelihoods under uncertainty, a Fuzzy Bayesian Network (FBN) is developed by combining expert fuzzy evaluations with historical accident data, enabling probabilistic inference, backward reasoning, and sensitivity analysis to reveal dominant risk factors and critical causal chains. Meanwhile, Analytic Hierarchy Process (AHP) is used to evaluate the consequence severity across the human, equipment, environment, and management dimensions, forming a multidimensional severity assessment system. Finally, accident likelihood and severity are integrated within a risk matrix based on the As Low As Reasonably Practicable (ALARP) principle to classify overall risk levels. The findings provide scientific support for safety optimization, accident prevention, and emergency management of HRSs, contributing to the safe and sustainable development of the hydrogen energy industry.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"100 ","pages":"Article 105865"},"PeriodicalIF":4.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145621869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risk management model for long-distance pipelines based on multi-dimensional safety barriers: An analytical framework in control measures research","authors":"Qian Wang ,&nbsp;Fanjie Liang ,&nbsp;Weichun Chang ,&nbsp;Ruipeng Tong","doi":"10.1016/j.jlp.2025.105910","DOIUrl":"10.1016/j.jlp.2025.105910","url":null,"abstract":"<div><div>The nonlinear interactions of human, machine, environmental, and management factors within long-distance pipeline systems constitute complex scenarios of risk evolution. However, systematic research on the types, functions, and models of safety barriers applicable to risk management has not yet been carried out. Therefore, this study developed a risk management model that incorporates multiple strategies including hard measures, soft measures, support measures, and emergency measures based on the multi-dimensional safety barriers of engineering technology, maintenance, personnel operations, and emergency measures. First, through expert consultations and systematic coding, we analyzed fundamental attributes, including spatiotemporal distribution, risk characteristics, and control measures, of 2013 safety incidents from China's largest pipeline enterprise, and applied cluster analysis to systematically classify these incidents. Second, we used Social Network Analysis (SNA) to explore the network topology of risk factors and control measures across different safety incident types, thereby identifying critical control measures in the overall complex system. Finally, the integrated weights of the control measures were determined by combining the Analytic Hierarchy Process (AHP) with centrality metrics, thereby quantifying the effectiveness of control measures in the risk management model. The results show that static equipment, management execution, procedure compliance, and source control are critical control measures in the pipeline system risk framework, with calculated weights of 0.118, 0.115, 0.115, and 0.114, respectively. This study promotes a paradigm shift in risk management from linear management measures to systemic safety barriers.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"100 ","pages":"Article 105910"},"PeriodicalIF":4.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hybrid deep learning model driven by physical mechanisms and data for predicting corrosion in natural gas pipelines","authors":"Peng Zhang ,&nbsp;Chuan Wang ,&nbsp;Wei Liu ,&nbsp;Haoyu Su","doi":"10.1016/j.jlp.2025.105852","DOIUrl":"10.1016/j.jlp.2025.105852","url":null,"abstract":"<div><div>In order to address the challenges posed by complex feature correlations, high uncertainty, and insufficient model generalization in predicting the corrosion depth of natural gas pipelines under small sample conditions, this paper proposes a hybrid deep learning framework that integrates physical mechanisms with data-driven approaches. The framework utilizes a Bayesian Network (BN) to identify seven critical features and constructs six interactive features based on physical-electrochemical corrosion mechanisms to enhance physical consistency. The model employs a three-stage architecture: XGBoost serves as the baseline model to learn global nonlinear trends and generate initial predictions. The Kolmogorov-Arnold Network (KAN) is first embedded to perform high-order feature modeling on the residuals of corrosion predictions, enhancing stable representation capabilities. The Gaussian Process (GP) performs residual smoothing correction in the embedded space and outputs a 95 % confidence interval. Validation based on 242 sets of sample data collected from excavation sites of buried pipelines in southern Mexico that have been in service for over 50 years.The findings indicate that by employing Bayesian methods for joint hyperparameter adjustment, the model attains a prediction performance of R² = 0.9613 and a root mean square error (RMSE) of 0.2809 on a dataset comprising 242 groups. This enhancement in prediction accuracy is accompanied by a reduction in RMSE of over 50 % when compared to a solitary XGB model. A high R² value indicates that the model possesses exceptional explanatory power and predictive accuracy, while the 95 % confidence interval provides reliable uncertainty boundaries for corrosion risk assessment and safety margin determination in engineering practice. The interpretability of the model was enhanced through the implementation of Shapley Additive Explanations (SHAP) and KAN weight analysis, which facilitated the visualization of both global and local feature contributions. The findings suggest that the water content (wc), dissolved chloride ions (cc), pH, and the interaction feature wc_rp exert a substantial influence on pipeline corrosion. This model achieves a balance between predictive accuracy, interpretability, and uncertainty quantification capabilities, thereby providing a reliable foundation for decision-making processes regarding pipeline corrosion monitoring and maintenance in scenarios involving small sample sizes.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"100 ","pages":"Article 105852"},"PeriodicalIF":4.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145691439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CFD analysis and parameter optimization of explosion suppression powder injection in a bag filter","authors":"Xiangbao Meng ,&nbsp;Dengzhao Li ,&nbsp;Jun Yuan ,&nbsp;Shanshan Liu","doi":"10.1016/j.jlp.2025.105895","DOIUrl":"10.1016/j.jlp.2025.105895","url":null,"abstract":"<div><div>The accumulation of combustible dust in industrial bag filters poses severe explosion risks. Automatic suppressant powder injection serves as a key protective measure, whose effectiveness hinges critically on the injection flow field and the uniformity of suppressant dispersion. This study employs a transient gas-solid two-phase CFD model in ANSYS Fluent to investigate an industrial bag filter. Based on the Euler-Lagrange framework and the Discrete Phase Model (DPM), flow field characteristics and suppressant dispersion patterns were systematically analyzed under three injection directions (downwind, upwind, sideward) and two pressures (4 MPa and 5 MPa). The coefficient of variation (COV) was introduced to quantify distribution uniformity. Results indicate that the downwind injection at 5 MPa achieves an optimal balance between coverage and stability, yielding a bottom average dust concentration of 0.85 kg/m³ and a COV of 0.33. The sideward injection at 4 MPa offers better flow stability (COV = 0.42) albeit with slightly lower coverage. In contrast, the upwind 5 MPa condition is the least favorable, as intense vortices induce suppressant re-suspension. These findings provide a theoretical basis and direct parametric guidance for the explosion-proof design and optimization of bag filter systems.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"100 ","pages":"Article 105895"},"PeriodicalIF":4.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145836661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design of an integrated firefighting suit with hazardous gas monitoring and early warning applying a time series model","authors":"Yiwei Peng ,&nbsp;Wenguo Weng ,&nbsp;Xinyan Huang ,&nbsp;Zhichao He","doi":"10.1016/j.jlp.2025.105894","DOIUrl":"10.1016/j.jlp.2025.105894","url":null,"abstract":"<div><div>Fire accident environments expose firefighters to life-threatening hazardous gases such as CO, HCN, and HCl, which can cause asphyxiation, organ damage, or even fatalities. Despite advancements in protective gear, conventional firefighting suits primarily offer passive protection, lacking real-time hazard forecasting. This reactive paradigm often results in delayed warnings against dynamic gas threats. This study proposes an innovative hardware-software integrated firefighting suit designed for proactive safety. The system combines wearable multi-gas sensors, edge computing, and a time series prediction model to forecast gas concentrations with 96.25 % accuracy. By analyzing historical data trends, the suit dynamically classifies hazard levels using a human vulnerability probit model, enabling proactive risk mitigation. Experimental results from simulated fire scenarios demonstrate superior performance in predicting concentrations of gases like H₂S and CO. The integration of predictive algorithms with real-time monitoring shifts safety management from passive response to proactive decision-making, enhancing firefighter survivability and operational efficiency. This advancement lays the foundation for next-generation intelligent firefighting equipment. This study is expected to provide a basis for the design of a kind of active protective firefighting suit.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"100 ","pages":"Article 105894"},"PeriodicalIF":4.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145836689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Safety-II theory based method for human reliability assessment to prevent fire and explosion during shipping LNG offloading","authors":"Renyou Zhang ,&nbsp;Mengjie Shi ,&nbsp;Rabiul Islam ,&nbsp;Shanguang Chen ,&nbsp;Shaowen Ding ,&nbsp;Zhen Liao ,&nbsp;Zhan Dou","doi":"10.1016/j.jlp.2026.105920","DOIUrl":"10.1016/j.jlp.2026.105920","url":null,"abstract":"<div><div>Human factors constitute the important element in ensuring operational safety in high-risk industries. Particularly in complex operations such as shipping Liquefied Natural Gas (LNG) offloading, even minor operational errors can cause disastrous consequences such as fire and explosion. Traditional accident causation theories often focus excessively on specific and localized causes, overlooking the intricate interconnections among components involved in complex tasks. This oversight can result in an inaccurate safety analysis model and questionable quantitative outcomes. Therefore, this study adopts the Safety-II theoretical framework, providing a perspective for understanding dynamic human-factor interactions in complex systems. Firstly, this study employs the Functional Resonance Analysis Method and Minimum Spanning Tree (FRAM-MST) algorithm to identify the critical functional coupling. Building upon this framework, the Cognitive Reliability and Error Analysis Method (CREAM) and Bayesian network (BN) are introduced to perform a quantitative risk analysis of unsafe behavior, assessing them from a probability perspective and calculating the Human Error Probability (HEP). The findings indicate that HEP for the LNG offloading operation is 1.52 × 10⁻⁴. This outcome provides operators with a clearer understanding of the risks associated with the operations, enabling the development of targeted explosion-proof measures.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"100 ","pages":"Article 105920"},"PeriodicalIF":4.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explainable AI-driven predictive maintenance for mitigating process safety risks in safety-critical industrial equipment","authors":"Zainab Ali Bu Sinnah","doi":"10.1016/j.jlp.2025.105907","DOIUrl":"10.1016/j.jlp.2025.105907","url":null,"abstract":"<div><div>Artificial Intelligence (AI) is increasingly transforming process safety by enabling early detection of equipment failures that could escalate into fires, explosions, or toxic releases. This study presents an interpretable hybrid machine learning framework that integrates ensemble tree classifiers with bio-inspired optimization algorithms for predictive maintenance in industrial settings. While demonstrated on CNC machinery, the framework is generalizable to safety-critical process equipment, enabling early detection of operational anomalies that could potentially escalate into process safety hazards. Using a twelve-month dataset of 2500 operating cycles from machinery representative of chemical and process plants, recursive feature elimination identified seven key process variables: hydraulic and coolant pressures, coolant and hydraulic-oil temperatures, spindle speed, torque, and cutting force that capture essential thermomechanical behavior associated with unsafe operating conditions. The hybrid models, validated through stratified 5-fold cross-validation, achieved test accuracies exceeding 0.98 and demonstrated robustness to industrial variability. Fourier Amplitude Sensitivity Test (FAST) analysis provided transparent, physically interpretable insights, highlighting torque and hydraulic pressure as dominant predictors of potential process hazards, while revealing synergistic effects of spindle speed and cutting force. By combining real-world sensor data, advanced optimization, and explainable AI, this framework enables proactive identification of safety-critical equipment degradation, supports inherently safer operations, and addresses key challenges of trustworthiness and interpretability in AI for process safety.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":"100 ","pages":"Article 105907"},"PeriodicalIF":4.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}